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Rebuilding the Social Compact: Urban Service Delivery and Property Taxes in Pakistan
Last registered on August 25, 2019

Pre-Trial

Trial Information
General Information
Title
Rebuilding the Social Compact: Urban Service Delivery and Property Taxes in Pakistan
RCT ID
AEARCTR-0003270
Initial registration date
August 28, 2018
Last updated
August 25, 2019 10:25 PM EDT
Location(s)

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Primary Investigator
Affiliation
MIT
Other Primary Investigator(s)
PI Affiliation
MIT
PI Affiliation
Harvard Kennedy School
PI Affiliation
LSE
Additional Trial Information
Status
On going
Start date
2016-05-01
End date
2019-12-31
Secondary IDs
Abstract
A significant challenge to the provision of local public services such as water, sanitation, waste removal, etc. in developing economies is the inability to raise adequate resources, especially through local taxation. In many countries the social compact, whereby citizens agree to pay taxes to fund government services that are then credibly and transparently delivered, is broken. A low willingness to pay taxes leads to low revenue collection and prevents adequate service provision, which in turn reduces willingness to pay and even leads to citizen disengagement from the state. In this project, we investigate whether strengthening the link between local collections and urban services can increase citizens’ willingness to pay for services, improve service delivery, and ultimately revitalize the social compact. We test this in major urban centers in Punjab, Pakistan via several interventions, including eliciting citizen preferences for specific services when taxes are collected and earmarking revenue for specific services, that credibly strengthen the link between tax collection and urban service provision. In addition to alleviating public finance challenges, outcomes will also address citizen demand, collective action, and broader political economy constraints.
External Link(s)
Registration Citation
Citation
Khan, Adnan et al. 2019. "Rebuilding the Social Compact: Urban Service Delivery and Property Taxes in Pakistan." AEA RCT Registry. August 25. https://doi.org/10.1257/rct.3270-2.0.
Former Citation
Khan, Adnan et al. 2019. "Rebuilding the Social Compact: Urban Service Delivery and Property Taxes in Pakistan." AEA RCT Registry. August 25. https://www.socialscienceregistry.org/trials/3270/history/52294.
Experimental Details
Interventions
Intervention(s)
Intervention Start Date
2016-11-30
Intervention End Date
2019-09-01
Primary Outcomes
Primary Outcomes (end points)
Tax assessments and payments; taxpayer morale; service provision; service quality; service use; attitudes towards government; voter behavior. (Tax assessments and payments are described in detail in attached pre-analysis plan.)
Primary Outcomes (explanation)
Secondary Outcomes
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
The study is a randomized controlled trial implemented in collaboration with the Government of Punjab,
Pakistan. The urban services examined are provided by local governments funded through locally collected
property taxes and fiscal transfers. The study aims to strengthen the link between taxes paid and the services
provided through the following main interventions:

1. Local Allocation. In the status quo, revenue is collected from administrative tax units and transferred
to local governments that allocate these to city-level services. However, there is no linkage between taxes
paid and services received at a lower and likely more salient geographical unit – the neighborhood (a
contiguous set of typically 100-400 households). To strengthen the link between taxes paid and services
provided, local governments will commit to allocate a portion (35%) of property tax collected from a
neighborhood to that same neighborhood. Citizens will be made aware of this linkage via videos shown on a
smartphone app and flyers distributed by the tax authority to households.

2. Voice. Tax staff will inform citizens of the tax-service linkage and give them a more direct voice in how
their taxes will be utilized by soliciting citizens’ preferences on which types of local goods and services should
be prioritized in their neighborhood. The results of this preference elicitation will be shared with the local
government in an effort to improve the allocation of services.

3. Voice-based Local Allocation. This intervention combines the previous two. By both eliciting citizen
preferences and requiring local governments to allocate a portion of property tax collected from a
neighborhood to that same neighborhood in accordance with these preferences, it seeks to make the tax-
services link even more salient and credible. Citizens will be informed of this earmarking, and the subsequent
service expenditures will be carried out in their locality.

In order to understand whether the (local) political process can enhance these impacts, we also cross-
randomize an additional intervention that enables local politicians to directly support the effectiveness of
these three schemes.

4. Local Politicians. This intervention is cross-randomized with all three interventions: Local Allocation,
Voice, and Voice-based Local Allocation. The local politicians are members of Union Councils, local
government bodies responsible for monitoring public services, dispute resolution, and for delivering certain
municipal services. They are both the closest and most accessible political actor for the citizen, and, given
their resources and knowledge, an effective intermediary between citizens and state.

Local politicians selected for this intervention will be allowed to intervene at different stages, depending on
the treatment status of a neighbourhood within their constituency: (1) In Voice and Voice-based Local
Allocation neighbourhoods, local politicians will introduce the intervention to taxpayers during town hall
meetings; (2) In Voice and Voice-based Local Allocation neighbourhoods, local politicians will monitor tax staff
as they collect taxpayer preferences; (3) In Local Allocation and Voice-based Local Allocation neighbourhoods,
these politicians will monitor and facilitate service delivery, using existing channels to pressure service
providers and assisting providers in selecting service locations; (4) finally, in Local Allocation and Voice-based
Local Allocation neighbourhoods, local politicians will hold public events to inaugurate new services and
reinforce the link between taxes and services.
Experimental Design Details
Not available
Randomization Method
Public lottery (neighborhood ballots); randomization done in office by a computer (property-level ballots).
Randomization Unit
Randomization for the main interventions is at the neighborhood level. We also randomized the content of the information and preference elicitation at the property level to help understand how to make the information credible and how to best get citizen voice. In particular, we vary who will deliver the information in the video (a high ranking politician or bureaucrat), whether taxpayers rate the quality of existing services in addition to providing preferences on new services, and whether taxpayers are given the opportunity to provide unstructured feedback to the government.
Was the treatment clustered?
Yes
Experiment Characteristics
Sample size: planned number of clusters
500 neighborhoods.
Sample size: planned number of observations
We observe administrative data for approximately 100,000 properties; we collect survey data for approximately 5,000 properties.
Sample size (or number of clusters) by treatment arms
150 neighborhoods control, 100 neighborhoods voice, 100 neighborhoods local allocation, 150 neighborhoods voice-based local allocation.

(The local politician treatment is cross-randomized with the voice, local allocation, and voice-based local allocation neighborhoods.)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Massachusetts Institute of Technology: Committee on the Use of Humans as Experimental Subjects
IRB Approval Date
2016-12-20
IRB Approval Number
1612793533
IRB Name
Massachusetts Institute of Technology: Committee on the Use of Humans as Experimental Subjects
IRB Approval Date
2016-04-14
IRB Approval Number
1004003834
Analysis Plan

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